GMGalaxies

About

Two galaxies: M83 and M87. Credit: NASA/ESA/Hubble Heritage Team (StScI/AURA)GMGalaxies is a research programme investigating the relationship between the history of cosmic structures and their properties which can be measured from images in telescopes. For example, what happened in the history of some galaxies to transform them into passive ellipticals while others, seemingly of the same mass and in the same environment, are star-forming spirals? Even such a basic question about the link between morphology and star formation has not yet been answered, revealing our theories of galaxy formation are inadequate. This is a major concern in an era where understanding the shapes of galaxies and how they relate to the underlying dark matter is essential for progress in precision cosmology.

Current research in this area rightly gives significant attention to the crucial problem of how feedback — energy input from supernovae, active galactic nuclei, and more — affect observable properties. But as well as investigating this avenue, the GMGalaxies team have pioneered and now continue to develop and apply a new technique (“genetic modification”) to investigate systematically the role of a galaxy’s merging and accretion history at high resolution, and to enhance our understanding of large scale structure in the Universe.

The genetic modification technique involves generating multiple, slightly different sets of early-Universe conditions from which a given galaxy, halo, or void will emerge. As each version of the Universe is evolved in its own computer simulation, the initial differences lead to contrasting evolutions – for instance, the galaxy might be formed earlier or later in the Universe's history, or undergo a different number of mergers with other galaxies. All this takes place in a fully cosmological setting, replicating the accretion of gas and dark matter along filaments.

The team has now published a large number of papers applying this technique, and the code has recently been made available to replicate and build on our studies.

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Publications

  1. Lucie-Smith et al. (2025) Cosmological feedback from a halo assembly perspective, PhRvD, 6, 063541 (full text)
  2. Taylor et al. (2025) The emergence of globular clusters and globular-cluster-like dwarfs, Nature, 8080, 327 (full text)
  3. Newton et al. (2025) The formation and disruption of globular cluster populations in simulations of present-day L* galaxies with controlled assembly histories, MNRAS, 2, 591 (full text)
  4. Rey et al. (2025) EDGE: the emergence of dwarf galaxy scaling relations from cosmological radiation-hydrodynamics simulations, MNRAS, 2, 1195 (full text)
  5. Gray et al. (2025) EDGE: a new model for nuclear star cluster formation in dwarf galaxies, MNRAS, 2, 1167 (full text)
  6. Joshi et al. (2025) The PARADIGM project - I. How early merger histories shape the present-day sizes of Milky-Way-mass galaxies, MNRAS, 4, 3792 (full text)
  7. Muni et al. (2025) EDGE: dark matter core creation depends on the timing of star formation, MNRAS, 1, 314 (full text)
  8. Andersson et al. (2025) EDGE-INFERNO: Simulating Every Observable Star in Faint Dwarf Galaxies and Their Consequences for Resolved-star Photometric Surveys, ApJ, 2, 129 (full text)
  9. Kocjan et al. (2024) Hot gas accretion fuels star formation faster than cold accretion in high-redshift galaxies, MNRAS, 1, 918 (full text)
  10. Guo et al. (2024) Deep learning insights into non-universality in the halo mass function, MNRAS, 4, 4141 (full text)
  11. Brooks et al. (2024) Action and energy clustering of stellar streams in deforming Milky Way dark matter haloes, MNRAS, 2, 2657 (full text)
  12. Oppenheim, Panella, and Pontzen (2024) Emergence of phantom cold dark matter from spacetime diffusion, arXiv, arXiv:2407.13820 (full text)
  13. Stopyra et al. (2024) An antihalo void catalogue of the Local Super-Volume, MNRAS, 2, 2213 (full text)
  14. Rey et al. (2024) EDGE - Dark matter or astrophysics? Breaking dark matter heating degeneracies with H I rotation in faint dwarf galaxies, MNRAS, 3, 2379 (full text)
  15. Lucie-Smith et al. (2024) Deep learning insights into cosmological structure formation, PhRvD, 6, 063524 (full text)
  16. Joshi et al. (2024) VINTERGATAN-GM: How do mergers affect the satellite populations of MW-like galaxies?, MNRAS, 2, 2346 (full text)
  17. Lucie-Smith, Peiris, and Pontzen (2024) Explaining Dark Matter Halo Density Profiles with Neural Networks, PhRvL, 3, 031001 (full text)
  18. Muni et al. (2024) From particles to orbits: precise dark matter density profiles using dynamical information, MNRAS, 3, 9250 (full text)
  19. Davies, Pontzen, and Crain (2024) Are the fates of supermassive black holes and galaxies determined by individual mergers, or by the properties of their host haloes?, MNRAS, 3, 4705 (full text)
  20. Stopyra et al. (2024) Towards accurate field-level inference of massive cosmic structures, MNRAS, 1, 1244 (full text)
  21. Piras et al. (2023) A robust estimator of mutual information for deep learning interpretability, MLS&T, 2, 025006 (full text)
  22. Rey et al. (2023) VINTERGATAN-GM: The cosmological imprints of early mergers on Milky-Way-mass galaxies, MNRAS, 1, 995 (full text)
  23. Cadiou, Pontzen, and Peiris (2022) Stellar angular momentum can be controlled from cosmological initial conditions, MNRAS, 3, 3459 (full text)
  24. Davies, Pontzen, and Crain (2022) Galaxy mergers can initiate quenching by unlocking an AGN-driven transformation of the baryon cycle, MNRAS, 1, 1430 (full text)
  25. Orkney et al. (2022) EDGE: the puzzling ellipticity of Eridanus II's star cluster and its implications for dark matter at the heart of an ultra-faint dwarf, MNRAS, 1, 185 (full text)
  26. Prgomet et al. (2022) EDGE: The sensitivity of ultra-faint dwarfs to a metallicity-dependent initial mass function, MNRAS, 2, 2326 (full text)
  27. Lucie-Smith et al. (2022) Discovering the building blocks of dark matter halo density profiles with neural networks, PhRvD, 10, 103533 (full text)
  28. Rey et al. (2022) EDGE: What shapes the relationship between H I and stellar observables in faint dwarf galaxies?, MNRAS, 4, 5672 (full text)
  29. Cadiou et al. (2021) The causal effect of environment on halo mass and concentration, MNRAS, 1, 1189 (full text)
  30. Stopyra et al. (2021) Quantifying the rarity of the local super-volume, MNRAS, 4, 5425 (full text)
  31. Orkney et al. (2021) EDGE: two routes to dark matter core formation in ultra-faint dwarfs, MNRAS, 3, 3509 (full text)
  32. Cadiou, Pontzen, and Peiris (2021) Angular momentum evolution can be predicted from cosmological initial conditions, MNRAS, 4, 5480 (full text)
  33. Sanchez et al. (2021) One-Two Quench: A Double Minor Merger Scenario, ApJ, 2, 116 (full text)
  34. Pontzen et al. (2021) EDGE: a new approach to suppressing numerical diffusion in adaptive mesh simulations of galaxy formation, MNRAS, 2, 1755 (full text)
  35. Fletcher et al. (2021) The cosmic abundance of cold gas in the local Universe, MNRAS, 1, 411 (full text)
  36. Stopyra et al. (2021) GenetIC—A New Initial Conditions Generator to Support Genetically Modified Zoom Simulations, ApJS, 2, 28 (full text)
  37. Davies, Crain, and Pontzen (2021) Quenching and morphological evolution due to circumgalactic gas expulsion in a simulated galaxy with a controlled assembly history, MNRAS, 1, 236 (full text)
  38. Stopyra, Peiris, and Pontzen (2021) How to build a catalogue of linearly evolving cosmic voids, MNRAS, 3, 4173 (full text)
  39. Cruz et al. (2021) Self-interacting dark matter and the delay of supermassive black hole growth, MNRAS, 2, 2177 (full text)
  40. Rey et al. (2020) EDGE: from quiescent to gas-rich to star-forming low-mass dwarf galaxies, MNRAS, 2, 1508 (full text)
  41. Agertz et al. (2020) EDGE: the mass-metallicity relation as a critical test of galaxy formation physics, MNRAS, 2, 1656 (full text)
  42. Lucie-Smith, Peiris, and Pontzen (2019) An interpretable machine-learning framework for dark matter halo formation, MNRAS, 1, 331 (full text)
  43. Rey et al. (2019) EDGE: The Origin of Scatter in Ultra-faint Dwarf Stellar Masses and Surface Brightnesses, ApJL, 1, L3 (full text)
  44. Sanchez et al. (2019) Not So Heavy Metals: Black Hole Feedback Enriches the Circumgalactic Medium, ApJ, 1, 8 (full text)
  45. Rey, Pontzen, and Saintonge (2019) Sensitivity of dark matter haloes to their accretion histories, MNRAS, 2, 1906 (full text)
  46. Tremmel et al. (2019) Introducing ROMULUSC: a cosmological simulation of a galaxy cluster with an unprecedented resolution, MNRAS, 3, 3336 (full text)
  47. Anderson et al. (2019) Cosmological Hydrodynamic Simulations with Suppressed Variance in the Lyα Forest Power Spectrum, ApJ, 2, 144 (full text)
  48. Villaescusa-Navarro et al. (2018) Statistical Properties of Paired Fixed Fields, ApJ, 2, 137 (full text)
  49. Lucie-Smith et al. (2018) Machine learning cosmological structure formation, MNRAS, 3, 3405 (full text)
  50. Pontzen and Tremmel (2018) TANGOS: The Agile Numerical Galaxy Organization System, ApJS, 2, 23 (full text)
  51. Tremmel et al. (2018) Dancing to CHANGA: a self-consistent prediction for close SMBH pair formation time-scales following galaxy mergers, MNRAS, 4, 4967 (full text)
  52. Tremmel et al. (2018) Wandering Supermassive Black Holes in Milky-Way-mass Halos, ApJL, 2, L22 (full text)
  53. Rey and Pontzen (2018) Quadratic genetic modifications: a streamlined route to cosmological simulations with controlled merger history, MNRAS, 1, 45 (full text)
  54. Pontzen et al. (2017) How to quench a galaxy, MNRAS, 1, 547 (full text)
  55. Angulo and Pontzen (2016) Cosmological N-body simulations with suppressed variance, MNRAS, 1, L1 (full text)
  56. Pontzen et al. (2016) Inverted initial conditions: Exploring the growth of cosmic structure and voids, PhRvD, 10, 103519 (full text)
  57. Roth, Pontzen, and Peiris (2016) Genetically modified haloes: towards controlled experiments in ΛCDM galaxy formation, MNRAS, 1, 974 (full text)

Code

genetIC code logoThe primary code output from the GMGalaxies project is our initial conditions generator, genetIC, which is designed to create initial conditions for N-body and hydrodynamical zoom simulations which can be tweaked or ‘genetically modified’. The purpose is to make fine custom adjustments to the history and environment of a galaxy, and so enable its dependence on these factors to be investigated systematically.

GenetIC accomplishes this by generating random initial conditions and then allowing the user to specify the required variations. Changes can be made in a large variety of linear variables including, for example, the average dark matter overdensity in specified regions or sub-regions of a galaxy's Lagrangian patch. By choosing the modifications carefully, a selection of galaxies with different accretion histories can be efficiently ‘scanned’ using a relatively small number of simulations.

Assuming that you are interested in applying this technique, note that the final changes to a halo accretion history cannot be perfectly predicted from a given modification to the initial conditions. There is an art to guessing the best set of modifications to achieve a particular effect. It is important to verify that the modifications imposed have had the desired effect (probably using a cheap dark-matter-only simulation, before spending CPU time on hydrodynamics!). To see what is possible in practice and how to achieve it, take a look at our publications.

Download the latest release of the code and manual from the github releases page. The manual also includes instructions for running genetIC as a docker container.

Members of the GMGalaxies collaboration also maintain and develop analysis codes, pynbody and tangos.

pynbody logoPynbody is an analysis package for astrophysical N-body and hydrodynamical simulations, supporting Python 3.5 and later. It enables users to analyse their simulations without worrying about file formats. The code has been used in a variety of astrophysical domains, ranging from cosmology to star and planet formation, for the last decade.

tangos logoTangos builds on the foundation of pynbody (or equivalent analysis packages such as yt) to create rich, interactive databases summarising the results of a cosmological simulation. It is particularly crucial for the GMGalaxies team because it allows us to link information about the development of galaxies over cosmic time and across simulations with different ‘genotypes’.

For more information about using these codes, please visit their github pages.

topsy logoRecently we have also added a real-time GPU-based visualization code, topsy. Topsy uses pynbody to load files, then performs SPH rendering on the GPU to enable rapid scientific exploration.